add 1 and 2

2/1.py

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+import numpy as np
+import cv2 as cv
+import matplotlib.pyplot as plt
+from sympy import im
+
+
+def global_linear_transmation(im, c=0, d=255):
+    img = im.copy()
+    maxV = img.max()
+    minV = img.min()
+    if maxV == minV:
+        return np.uint8(img)
+    for i in range(img.shape[0]):
+        for j in range(img.shape[1]):
+            img[i, j] = ((d - c) / (maxV - minV)) * (img[i, j] - minV) + c
+    return np.uint8(img)
+
+
+def histogram_equalization(im):
+    return np.uint8(cv.equalizeHist(im))
+
+
+if __name__ == "__main__":
+    im = cv.imread(r"E:\OneDrive\Code\Python\2\iris.jpg", cv.IMREAD_GRAYSCALE)
+    im1 = global_linear_transmation(im, 0, 150)
+    im2 = global_linear_transmation(im, 100)
+    im3 = global_linear_transmation(im, 50, 150)
+    im4 = histogram_equalization(im)
+    plt.figure()
+    plt.subplot(241)
+    plt.imshow(im1, cmap="gray")
+    plt.title("darker")
+    plt.subplot(242)
+    plt.imshow(im2, cmap="gray")
+    plt.title("brighter")
+    plt.subplot(243)
+    plt.imshow(im3, cmap="gray")
+    plt.title("lower contrast")
+    plt.subplot(244)
+    plt.imshow(im4, cmap="gray")
+    plt.title("equalized")
+    plt.subplot(245)
+    plt.hist(im1.flatten(), 256, [0, 256])
+    plt.subplot(246)
+    plt.hist(im2.flatten(), 256, [0, 256])
+    plt.subplot(247)
+    plt.hist(im3.flatten(), 256, [0, 256])
+    plt.subplot(248)
+    plt.hist(im4.flatten(), 256, [0, 256])
+    plt.show()

2/2.py

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+import numpy as np
+import cv2 as cv
+import matplotlib.pyplot as plt
+
+
+def gamma_trans(img, gamma=1.0):
+    gamma_table = [np.power(x / 255.0, gamma) * 255.0 for x in range(256)]
+    gamma_table = np.round(np.array(gamma_table)).astype(np.uint8)
+    return cv.LUT(img, gamma_table)
+
+
+if __name__ == "__main__":
+    im = cv.imread(r"E:\OneDrive\Code\Python\2\iris.jpg", cv.IMREAD_GRAYSCALE)
+    im1 = gamma_trans(im, 0.5)
+    im2 = gamma_trans(im, 1.5)
+    plt.figure()
+    plt.subplot(131)
+    plt.imshow(im, cmap="gray")
+    plt.title("original")
+    plt.subplot(132)
+    plt.imshow(im1, cmap="gray")
+    plt.title("gamma = 0.5")
+    plt.subplot(133)
+    plt.imshow(im2, cmap="gray")
+    plt.title("gamma = 1.5")
+    plt.show()

2/3.py

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+import cv2 as cv
+from matplotlib import pyplot as plt
+import numpy as np
+
+img = cv.imread(r"E:\OneDrive\Code\Python\2\iris.jpg", 0)
+fil1 = 1 / 16 * np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]])
+fil2 = 1 / 9 * np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]])
+fil3 = 1 / 10 * np.array([[1, 1, 1], [1, 2, 1], [1, 1, 1]])
+fil4 = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
+ImgSmoothed1 = cv.filter2D(img, -1, fil1, borderType=cv.BORDER_DEFAULT)
+ImgSmoothed2 = cv.filter2D(img, -1, fil2, borderType=cv.BORDER_DEFAULT)
+ImgSmoothed3 = cv.filter2D(img, -1, fil3, borderType=cv.BORDER_DEFAULT)
+ImgSharp = cv.filter2D(img, -1, fil4, borderType=cv.BORDER_DEFAULT)
+
+plt.figure()
+plt.subplot(221)
+plt.imshow(ImgSmoothed1, cmap="gray")
+plt.title("smoothed1")
+plt.subplot(222)
+plt.imshow(ImgSmoothed2, cmap="gray")
+plt.title("smoothed2")
+plt.subplot(223)
+plt.imshow(ImgSmoothed3, cmap="gray")
+plt.title("smoothed3")
+plt.subplot(224)
+plt.imshow(ImgSharp, cmap="gray")
+plt.title("sharp")
+plt.show()

2/4.py

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+import random as rd
+import numpy as np
+import cv2 as cv
+import matplotlib.pyplot as plt
+
+
+def addSaltAndPepper(src, percentage):
+    NoiseImg = src.copy()
+    NoiseNum = int(percentage * src.shape[0] * src.shape[1])
+    for i in range(NoiseNum):
+        randX = rd.randint(0, src.shape[0] - 1)
+        randY = rd.randint(0, src.shape[1] - 1)
+        if rd.randint(0, 1) == 0:
+            NoiseImg[randX, randY] = 0
+        else:
+            NoiseImg[randX, randY] = 255
+    return NoiseImg
+
+
+def addGaussianNoise(src, means, sigma):
+    NoiseImg = src / src.max()
+    rows = NoiseImg.shape[0]
+    cols = NoiseImg.shape[1]
+    for i in range(rows):
+        for j in range(cols):
+            NoiseImg[i, j] = NoiseImg[i, j] + rd.gauss(means, sigma)
+            if NoiseImg[i, j] < 0:
+                NoiseImg[i, j] = 0
+            if NoiseImg[i, j] > 1:
+                NoiseImg[i, j] = 1
+    NoiseImg = np.uint8(NoiseImg * 255)
+    return NoiseImg
+
+
+if __name__ == "__main__":
+    im = cv.imread(r"E:\OneDrive\Code\Python\2\iris.jpg", cv.IMREAD_GRAYSCALE)
+    im1 = addSaltAndPepper(im, 0.1)
+    im11 = cv.blur(im1, (3, 3))
+    im12 = cv.medianBlur(im1, 3)
+    im13 = cv.GaussianBlur(im1, (3, 3), 1)
+    im2 = addGaussianNoise(im, 0, 0.1)
+    im21 = cv.blur(im2, (3, 3))
+    im22 = cv.medianBlur(im2, 3)
+    im23 = cv.GaussianBlur(im2, (3, 3), 1)
+    plt.figure()
+    plt.subplot(241)
+    plt.imshow(im1, cmap="gray")
+    plt.title("salt and pepper")
+    plt.subplot(242)
+    plt.imshow(im11, cmap="gray")
+    plt.title("blur")
+    plt.subplot(243)
+    plt.imshow(im12, cmap="gray")
+    plt.title("median")
+    plt.subplot(244)
+    plt.imshow(im13, cmap="gray")
+    plt.title("gaussian")
+    plt.subplot(245)
+    plt.imshow(im2, cmap="gray")
+    plt.title("gaussian noise")
+    plt.subplot(246)
+    plt.imshow(im21, cmap="gray")
+    plt.title("blur")
+    plt.subplot(247)
+    plt.imshow(im22, cmap="gray")
+    plt.title("median")
+    plt.subplot(248)
+    plt.imshow(im23, cmap="gray")
+    plt.title("gaussian")
+    plt.show()

2/5.py

@@ -0,0 +1,21 @@
+import cv2 as cv
+from matplotlib import pyplot as plt
+import numpy as np
+
+img = cv.imread(r"E:\OneDrive\Code\Python\2\iris.jpg", 0)
+lplc = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])
+lplcEnhance = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
+ImgLplc = cv.filter2D(img, -1, lplc, borderType=cv.BORDER_DEFAULT)
+ImgLplcEnhance = cv.filter2D(img, -1, lplcEnhance, borderType=cv.BORDER_DEFAULT)
+
+plt.figure()
+plt.subplot(131)
+plt.imshow(img, cmap="gray")
+plt.title("original")
+plt.subplot(132)
+plt.imshow(ImgLplc, cmap="gray")
+plt.title("laplacian")
+plt.subplot(133)
+plt.imshow(ImgLplcEnhance, cmap="gray")
+plt.title("laplacian enhanced")
+plt.show()